Preferences and Partial Satisfaction in
Planning

Planning research has traditionally focused on scenarios in which all
stated goals are treated as hard constraints that must be met to
satisfy the problem. However, many real-world planning applications
often require the consideration of user preferences. Preferences,
unlike hard goals, are properties of a plan that are desired in a
solution, but not enforced. Example applications range from robotics
(in which robots are expected to respect as much as
possible their user's preferences) and software applications (e.g.,
component software composition, web service composition). Preferences
are also useful in applications in which certain goals are allowed to
remain unachieved due lack of resources. In recent years, much
attention has been given to solving these types of problems within
the planning community which involve issues of plan quality
optimization, as opposed to only finding a satisficing plan. This new
interest is evidenced by the recent preference and net benefit tracks
in the respective 2006 and 2008 International Planning
Competitions. Furthermore, much recent research has begun to focus on
solving for temporally extended preferences over a state trajectory,
handling preferences in HTNs and in partial satisfaction planning on
which the net benefit competition track was based.

In this tutorial, we will examine the current
state-of-the-art in preference-based, partial satisfaction planning
and will cover representation of preferences and soft constraints,
defining objective functions, frameworks for solving for
preference-based and over-subscription planning problems, and current
open challenges in preference and over-subscription planning